A method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively.

Oxypred statistics

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Oxypred in publication

[…] eukaryotes as well as in fungi, plants, and animals. the exponential growth and availability of fresh annotated protein sequences in the databases motivated us to develop an improved version of “oxypred” for identifying oxygen-binding proteins., in this study, we have proposed a method for identifying oxy-proteins with two different sequence similarity cutoffs 50 and 90%. a different amino […]

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